Fetching the paper…
Reading the bibliography…
Deep Reinforcement Learning (DRL) has recently spread into a range of domains within physics and engineering, with multiple remarkable achievements.
A numerical method for the design of camber surfaces of supersonic wings with arbitrary planforms
H. W. Carlson and W. D. Middleton · 1964
Earlier work this paper cites.
On optimum design in fluid mechanics
O. Pironneau · 1974
Earlier work this paper cites.
Aerodynamics of road vehicles
Wolf Hucho and Gino Sovran · 1993
Earlier work this paper cites.
Shape optimization by the homogenization method
Grégoire Allaire, Eric Bonnetier, Gilles Francfort, and François Jouve · 1997
Earlier work this paper cites.
Policy Gradient Methods for Reinforcement Learning with Function Approximation Richard
Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour · 2000
Earlier work this paper cites.
Shape optimization for aerodynamic noise control
Alison L Marsden, Meng Wang, Bijan Mohammadi, and P Moin · 2001
Earlier work this paper cites.
Application of simulated annealing to inverse design of transonic turbomachinery cascades
W. T. Tiow, K. F C. Yiu, and M Zangeneh · 2002
Earlier work this paper cites.
Aerodynamic shape optimization using the Adjoint Method
Antony Jameson · 2003
Earlier work this paper cites.
Surrogate-based analysis and optimization
Nestor V. Queipo, Raphael T. Haftka, Wei Shyy, Tushar Goel, Rajkumar Vaidyanathan, and P. Kevin Tucker · 2005
Earlier work this paper cites.
An overview of projection methods for incompressible flows
J. L. Guermond, P. Minev, and Jie Shen · 2006
Earlier work this paper cites.
Aerodynamic design optimization using the drag-decomposition method
W. Yamazaki, K. Matsushima, and K. Nakahashi · 2008
Earlier work this paper cites.
Multiconstrained aerodynamic design of business jet by cfd driven optimization tool
Sergey Peigin and Boris Epstein · 2008
Earlier work this paper cites.
Morphing Airfoils with Four Morphing Parameters
Amanda Lampton, Adam Niksch, and John Valasek · 2008
Earlier work this paper cites.
Airfoil/Wing Optimization
Thomas A. Zang · 2010
Earlier work this paper cites.
Reinforcement learning of a morphing airfoil-policy and discrete learning analysis
Amanda Lampton, Adam Niksch, and John Valasek · 2010
Earlier work this paper cites.
Robust airfoil optimization based on improved particle swarm optimization method
Yuan-yuan Wang, Bin-qian Zhang, and Ying-chun Chen · 2011
Cited alongside, same era.
A comparison of particle swarm optimization and the genetic algorithm
Rania Hassan, Babak Cohanim, Olivier de Weck, and Gerhard Venter · 2012
Cited alongside, same era.
Aerodynamic shape optimization of complex aircraft configurations via an adjoint formulation
J. Reuther, A. Jameson, J. Farmer, L. Martinelli, and D. Saunders · 2013
Cited alongside, same era.
Multimodality and global optimization in aerodynamic design
Oleg Chernukhin and David W. Zingg · 2013
Cited alongside, same era.
Atari Deep Reinforcement learning
Volodymyr Mnih, David Silver, and Martin Riedmiller · 2013
Cited alongside, same era.
Aerodynamic guidelines in the design and optimization of new regional turboprop aircraft
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando de Freitas · 2016
Later among the works it cites.
The Deep Learning Book
Aaron Courville Ian Goodfellow, Yoshua Bengio · 2017
Later among the works it cites.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Later among the works it cites.
Emergence of Locomotion Behaviours in Rich Environments
Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin Riedmiller, and David Silver · 2017
Later among the works it cites.
Proximal Policy Optimization Algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Later among the works it cites.
A three-dimensional finite element mesh generator with built-in pre- and post-processing facilities
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pierluigi Della Vecchia and Fabrizio Nicolosi · 2014
Cited alongside, same era.
Shape Optimization in Electromagnetic Applications
Johannes Semmler, Lukas Pflug, Michael Stingl, and Günter Leugering · 2015
Cited alongside, same era.
Multipoint Aerodynamic Shape Optimization Investigations of the Common Research Model Wing
Gaetan K. W. Kenway and Joaquim R. R. A. Martins · 2015
Cited alongside, same era.
The fenics project version 1.5
Martin S. Alnæs, Jan Blechta, Johan Hake, August Johansson, Benjamin Kehlet, Anders Logg, Chris Richardson, Johannes Ring, Marie E. Rognes, and Garth N. Wells · 2015
Cited alongside, same era.
Trust Region Policy Optimization
John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, and Pieter Abbeel · 2015
Cited alongside, same era.
High-Dimensional Continuous Control Using Generalized Advantage Estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
Cited alongside, same era.
Design optimization of composite radar absorbing structures to improve stealth performance
Byungwook Jang, Myungjun Kim, Jungsun Park, and Sooyong Lee · 2016
Cited alongside, same era.
Christophe Geuzaine and Jean-François Remacle · 2017
Later among the works it cites.
Tensorforce: a tensorflow library for applied reinforcement learning
Alexander Kuhnle, Michael Schaarschmidt, and Kai Fricke · 2017
Later among the works it cites.
State-of-the-art in aerodynamic shape optimisation methods
S. N. Skinner and H. Zare-Behtash · 2018
Later among the works it cites.
An Introduction to Deep Reinforcement Learning
Vincent François-lavet, Peter Henderson, Riashat Islam, and Marc G Bellemare · 2018
Later among the works it cites.
Superhuman ai for multiplayer poker
Noam Brown and Tuomas Sandholm · 2019
Closest in time.
Artificial Neural Networks trained through Deep Reinforcement Learning discover control strategies for active flow control
Jean Rabault, Miroslav Kuchta, Atle Jensen, Ulysse Reglade, and Nicolas Cerardi · 2019
Closest in time.
A review on deep reinforcement learning for fluid mechanics
Paul Garnier, Jonathan Viquerat, Jean Rabault, Aurélien Larcher, Alexander Kuhnle, and Elie Hachem · 2019
Closest in time.
Accelerating deep reinforcement learning of active flow control strategies through a multi-environment approach
Jean Rabault and Alexander Kuhnle · 2019
Closest in time.
Machine learning for fluid mechanics
Steven L. Brunton, Bernd R. Noack, and Petros Koumoutsakos · 2020
Closest in time.